Overview

Dataset statistics

Number of variables18
Number of observations22049
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.5 MiB
Average record size in memory546.3 B

Variable types

Text2
DateTime1
Numeric7
Categorical7
Boolean1

Alerts

Total_Amount is highly overall correlated with Unit_PriceHigh correlation
Unit_Price is highly overall correlated with Total_AmountHigh correlation
Order_ID has unique valuesUnique
Discount_Amount has 14046 (63.7%) zerosZeros

Reproduction

Analysis started2026-02-20 16:16:24.528120
Analysis finished2026-02-20 16:16:30.388267
Duration5.86 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Order_ID
Text

Unique 

Distinct22049
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2026-02-20T21:46:30.694283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.550728
Min length10

Characters and Unicode

Total characters254682
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22049 ?
Unique (%)100.0%

Sample

1st rowORD_000902
2nd rowORD_003209
3rd rowORD_001404
4th rowORD_000463
5th rowORD_002934
ValueCountFrequency (%)
ord_0009021
 
< 0.1%
ord_001329-51
 
< 0.1%
ord_0029341
 
< 0.1%
ord_0013601
 
< 0.1%
ord_0008411
 
< 0.1%
ord_0010481
 
< 0.1%
ord_0010511
 
< 0.1%
ord_0035431
 
< 0.1%
ord_003371-81
 
< 0.1%
ord_0044431
 
< 0.1%
Other values (22039)22039
> 99.9%
2026-02-20T21:46:30.967009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
055159
21.7%
O22049
 
8.7%
R22049
 
8.7%
D22049
 
8.7%
_22049
 
8.7%
-17049
 
6.7%
116163
 
6.3%
214947
 
5.9%
313932
 
5.5%
412956
 
5.1%
Other values (5)36280
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)254682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
055159
21.7%
O22049
 
8.7%
R22049
 
8.7%
D22049
 
8.7%
_22049
 
8.7%
-17049
 
6.7%
116163
 
6.3%
214947
 
5.9%
313932
 
5.5%
412956
 
5.1%
Other values (5)36280
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)254682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
055159
21.7%
O22049
 
8.7%
R22049
 
8.7%
D22049
 
8.7%
_22049
 
8.7%
-17049
 
6.7%
116163
 
6.3%
214947
 
5.9%
313932
 
5.5%
412956
 
5.1%
Other values (5)36280
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)254682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
055159
21.7%
O22049
 
8.7%
R22049
 
8.7%
D22049
 
8.7%
_22049
 
8.7%
-17049
 
6.7%
116163
 
6.3%
214947
 
5.9%
313932
 
5.5%
412956
 
5.1%
Other values (5)36280
14.2%
Distinct5000
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2026-02-20T21:46:31.201855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters220490
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCUST_00902
2nd rowCUST_03209
3rd rowCUST_01404
4th rowCUST_00463
5th rowCUST_02934
ValueCountFrequency (%)
cust_0257211
 
< 0.1%
cust_0084411
 
< 0.1%
cust_0328511
 
< 0.1%
cust_0358011
 
< 0.1%
cust_0417911
 
< 0.1%
cust_0123011
 
< 0.1%
cust_0276511
 
< 0.1%
cust_0215611
 
< 0.1%
cust_0437811
 
< 0.1%
cust_0159411
 
< 0.1%
Other values (4990)21939
99.5%
2026-02-20T21:46:31.491365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
033016
15.0%
C22049
10.0%
U22049
10.0%
S22049
10.0%
T22049
10.0%
_22049
10.0%
411111
 
5.0%
311078
 
5.0%
111069
 
5.0%
210839
 
4.9%
Other values (5)33132
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)220490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
033016
15.0%
C22049
10.0%
U22049
10.0%
S22049
10.0%
T22049
10.0%
_22049
10.0%
411111
 
5.0%
311078
 
5.0%
111069
 
5.0%
210839
 
4.9%
Other values (5)33132
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)220490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
033016
15.0%
C22049
10.0%
U22049
10.0%
S22049
10.0%
T22049
10.0%
_22049
10.0%
411111
 
5.0%
311078
 
5.0%
111069
 
5.0%
210839
 
4.9%
Other values (5)33132
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)220490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
033016
15.0%
C22049
10.0%
U22049
10.0%
S22049
10.0%
T22049
10.0%
_22049
10.0%
411111
 
5.0%
311078
 
5.0%
111069
 
5.0%
210839
 
4.9%
Other values (5)33132
15.0%

Date
Date

Distinct451
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size344.5 KiB
Minimum2023-01-01 00:00:00
Maximum2024-03-26 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-20T21:46:31.584436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:31.683432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Age
Real number (ℝ)

Distinct57
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.965441
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.5 KiB
2026-02-20T21:46:31.778761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q127
median35
Q342
95-th percentile54
Maximum75
Range57
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.054313
Coefficient of variation (CV)0.31614967
Kurtosis-0.39394124
Mean34.965441
Median Absolute Deviation (MAD)8
Skewness0.3239267
Sum770953
Variance122.19783
MonotonicityNot monotonic
2026-02-20T21:46:31.865883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182058
 
9.3%
37869
 
3.9%
34795
 
3.6%
38771
 
3.5%
32769
 
3.5%
40746
 
3.4%
35730
 
3.3%
41681
 
3.1%
27677
 
3.1%
28667
 
3.0%
Other values (47)13286
60.3%
ValueCountFrequency (%)
182058
9.3%
19289
 
1.3%
20359
 
1.6%
21348
 
1.6%
22387
 
1.8%
23503
 
2.3%
24453
 
2.1%
25569
 
2.6%
26544
 
2.5%
27677
 
3.1%
ValueCountFrequency (%)
7512
 
0.1%
734
 
< 0.1%
7212
 
0.1%
718
 
< 0.1%
709
 
< 0.1%
694
 
< 0.1%
6810
 
< 0.1%
679
 
< 0.1%
6628
0.1%
6532
0.1%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Female
11105 
Male
10611 
Other
 
333

Length

Max length6
Median length6
Mean length5.0224046
Min length4

Characters and Unicode

Total characters110739
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female11105
50.4%
Male10611
48.1%
Other333
 
1.5%

Length

2026-02-20T21:46:31.949172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T21:46:32.007261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female11105
50.4%
male10611
48.1%
other333
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e33154
29.9%
a21716
19.6%
l21716
19.6%
F11105
 
10.0%
m11105
 
10.0%
M10611
 
9.6%
O333
 
0.3%
t333
 
0.3%
h333
 
0.3%
r333
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)110739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e33154
29.9%
a21716
19.6%
l21716
19.6%
F11105
 
10.0%
m11105
 
10.0%
M10611
 
9.6%
O333
 
0.3%
t333
 
0.3%
h333
 
0.3%
r333
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)110739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e33154
29.9%
a21716
19.6%
l21716
19.6%
F11105
 
10.0%
m11105
 
10.0%
M10611
 
9.6%
O333
 
0.3%
t333
 
0.3%
h333
 
0.3%
r333
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)110739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e33154
29.9%
a21716
19.6%
l21716
19.6%
F11105
 
10.0%
m11105
 
10.0%
M10611
 
9.6%
O333
 
0.3%
t333
 
0.3%
h333
 
0.3%
r333
 
0.3%

City
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Istanbul
5686 
Ankara
3157 
Izmir
2672 
Bursa
2217 
Adana
1704 
Other values (5)
6613 

Length

Max length9
Median length8
Mean length6.6083723
Min length5

Characters and Unicode

Total characters145708
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKonya
2nd rowIzmir
3rd rowAdana
4th rowIstanbul
5th rowAnkara

Common Values

ValueCountFrequency (%)
Istanbul5686
25.8%
Ankara3157
14.3%
Izmir2672
12.1%
Bursa2217
 
10.1%
Adana1704
 
7.7%
Antalya1620
 
7.3%
Gaziantep1532
 
6.9%
Konya1437
 
6.5%
Kayseri1108
 
5.0%
Eskisehir916
 
4.2%

Length

2026-02-20T21:46:32.069997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T21:46:32.147723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
istanbul5686
25.8%
ankara3157
14.3%
izmir2672
12.1%
bursa2217
 
10.1%
adana1704
 
7.7%
antalya1620
 
7.3%
gaziantep1532
 
6.9%
konya1437
 
6.5%
kayseri1108
 
5.0%
eskisehir916
 
4.2%

Most occurring characters

ValueCountFrequency (%)
a26474
18.2%
n15136
10.4%
s10843
 
7.4%
r10070
 
6.9%
t8838
 
6.1%
I8358
 
5.7%
u7903
 
5.4%
l7306
 
5.0%
i7144
 
4.9%
A6481
 
4.4%
Other values (14)37155
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)145708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a26474
18.2%
n15136
10.4%
s10843
 
7.4%
r10070
 
6.9%
t8838
 
6.1%
I8358
 
5.7%
u7903
 
5.4%
l7306
 
5.0%
i7144
 
4.9%
A6481
 
4.4%
Other values (14)37155
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)145708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a26474
18.2%
n15136
10.4%
s10843
 
7.4%
r10070
 
6.9%
t8838
 
6.1%
I8358
 
5.7%
u7903
 
5.4%
l7306
 
5.0%
i7144
 
4.9%
A6481
 
4.4%
Other values (14)37155
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)145708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a26474
18.2%
n15136
10.4%
s10843
 
7.4%
r10070
 
6.9%
t8838
 
6.1%
I8358
 
5.7%
u7903
 
5.4%
l7306
 
5.0%
i7144
 
4.9%
A6481
 
4.4%
Other values (14)37155
25.5%

Product_Category
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Sports
2915 
Beauty
2833 
Books
2822 
Food
2722 
Toys
2700 
Other values (3)
8057 

Length

Max length13
Median length11
Mean length6.9646242
Min length4

Characters and Unicode

Total characters153563
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBooks
2nd rowFood
3rd rowBeauty
4th rowFood
5th rowToys

Common Values

ValueCountFrequency (%)
Sports2915
13.2%
Beauty2833
12.8%
Books2822
12.8%
Food2722
12.3%
Toys2700
12.2%
Electronics2698
12.2%
Home & Garden2681
12.2%
Fashion2678
12.1%

Length

2026-02-20T21:46:32.233863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T21:46:32.290036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sports2915
10.6%
beauty2833
10.3%
books2822
10.3%
food2722
9.9%
toys2700
9.9%
electronics2698
9.8%
home2681
9.8%
2681
9.8%
garden2681
9.8%
fashion2678
9.8%

Most occurring characters

ValueCountFrequency (%)
o24760
16.1%
s13813
 
9.0%
e10893
 
7.1%
t8446
 
5.5%
r8294
 
5.4%
a8192
 
5.3%
n8057
 
5.2%
B5655
 
3.7%
y5533
 
3.6%
d5403
 
3.5%
Other values (16)54517
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)153563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o24760
16.1%
s13813
 
9.0%
e10893
 
7.1%
t8446
 
5.5%
r8294
 
5.4%
a8192
 
5.3%
n8057
 
5.2%
B5655
 
3.7%
y5533
 
3.6%
d5403
 
3.5%
Other values (16)54517
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)153563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o24760
16.1%
s13813
 
9.0%
e10893
 
7.1%
t8446
 
5.5%
r8294
 
5.4%
a8192
 
5.3%
n8057
 
5.2%
B5655
 
3.7%
y5533
 
3.6%
d5403
 
3.5%
Other values (16)54517
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)153563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o24760
16.1%
s13813
 
9.0%
e10893
 
7.1%
t8446
 
5.5%
r8294
 
5.4%
a8192
 
5.3%
n8057
 
5.2%
B5655
 
3.7%
y5533
 
3.6%
d5403
 
3.5%
Other values (16)54517
35.5%

Unit_Price
Real number (ℝ)

High correlation 

Distinct18299
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449.70051
Minimum5.05
Maximum7900.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.5 KiB
2026-02-20T21:46:32.388037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.05
5-th percentile27.244
Q174.05
median177.11
Q3499.36
95-th percentile1794.946
Maximum7900.01
Range7894.96
Interquartile range (IQR)425.31

Descriptive statistics

Standard deviation720.09111
Coefficient of variation (CV)1.6012682
Kurtosis17.247287
Mean449.70051
Median Absolute Deviation (MAD)129.58
Skewness3.6071037
Sum9915446.5
Variance518531.21
MonotonicityNot monotonic
2026-02-20T21:46:32.477794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.638
 
< 0.1%
34.715
 
< 0.1%
46.785
 
< 0.1%
52.335
 
< 0.1%
60.185
 
< 0.1%
67.375
 
< 0.1%
23.735
 
< 0.1%
39.425
 
< 0.1%
83.635
 
< 0.1%
24.555
 
< 0.1%
Other values (18289)21996
99.8%
ValueCountFrequency (%)
5.051
< 0.1%
5.181
< 0.1%
5.781
< 0.1%
6.32
< 0.1%
6.351
< 0.1%
6.641
< 0.1%
6.651
< 0.1%
6.831
< 0.1%
7.581
< 0.1%
7.591
< 0.1%
ValueCountFrequency (%)
7900.011
< 0.1%
7570.411
< 0.1%
7440.421
< 0.1%
7272.851
< 0.1%
7159.451
< 0.1%
7061.341
< 0.1%
6935.921
< 0.1%
6810.271
< 0.1%
6766.071
< 0.1%
6759.751
< 0.1%

Quantity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
5641 
2
4431 
5
4027 
3
3997 
4
3953 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22049
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
15641
25.6%
24431
20.1%
54027
18.3%
33997
18.1%
43953
17.9%

Length

2026-02-20T21:46:32.550390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T21:46:32.607621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15641
25.6%
24431
20.1%
54027
18.3%
33997
18.1%
43953
17.9%

Most occurring characters

ValueCountFrequency (%)
15641
25.6%
24431
20.1%
54027
18.3%
33997
18.1%
43953
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)22049
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15641
25.6%
24431
20.1%
54027
18.3%
33997
18.1%
43953
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22049
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15641
25.6%
24431
20.1%
54027
18.3%
33997
18.1%
43953
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22049
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15641
25.6%
24431
20.1%
54027
18.3%
33997
18.1%
43953
17.9%

Discount_Amount
Real number (ℝ)

Zeros 

Distinct6463
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.598256
Minimum0
Maximum6538.29
Zeros14046
Zeros (%)63.7%
Negative0
Negative (%)0.0%
Memory size344.5 KiB
2026-02-20T21:46:32.677717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325.08
95-th percentile308.892
Maximum6538.29
Range6538.29
Interquartile range (IQR)25.08

Descriptive statistics

Standard deviation216.62132
Coefficient of variation (CV)3.6346922
Kurtosis144.23264
Mean59.598256
Median Absolute Deviation (MAD)0
Skewness9.395494
Sum1314081.9
Variance46924.795
MonotonicityNot monotonic
2026-02-20T21:46:32.757825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014046
63.7%
3.847
 
< 0.1%
7.886
 
< 0.1%
8.396
 
< 0.1%
18.956
 
< 0.1%
3.725
 
< 0.1%
7.145
 
< 0.1%
9.445
 
< 0.1%
8.445
 
< 0.1%
4.935
 
< 0.1%
Other values (6453)7953
36.1%
ValueCountFrequency (%)
014046
63.7%
0.621
 
< 0.1%
0.81
 
< 0.1%
0.861
 
< 0.1%
0.891
 
< 0.1%
0.971
 
< 0.1%
0.981
 
< 0.1%
1.011
 
< 0.1%
1.021
 
< 0.1%
1.031
 
< 0.1%
ValueCountFrequency (%)
6538.291
< 0.1%
5158.851
< 0.1%
5109.321
< 0.1%
4697.851
< 0.1%
4378.571
< 0.1%
4354.491
< 0.1%
4252.421
< 0.1%
3754.741
< 0.1%
3738.191
< 0.1%
3504.471
< 0.1%

Total_Amount
Real number (ℝ)

High correlation 

Distinct20098
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1210.6942
Minimum6.21
Maximum37852.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.5 KiB
2026-02-20T21:46:32.831038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.21
5-th percentile47.316
Q1159.8
median429.92
Q31199.06
95-th percentile5031.35
Maximum37852.05
Range37845.84
Interquartile range (IQR)1039.26

Descriptive statistics

Standard deviation2265.7556
Coefficient of variation (CV)1.8714515
Kurtosis31.510164
Mean1210.6942
Median Absolute Deviation (MAD)329.94
Skewness4.6772284
Sum26694597
Variance5133648.4
MonotonicityNot monotonic
2026-02-20T21:46:32.914444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172.985
 
< 0.1%
192.694
 
< 0.1%
141.884
 
< 0.1%
40.184
 
< 0.1%
120.184
 
< 0.1%
57.814
 
< 0.1%
77.564
 
< 0.1%
128.784
 
< 0.1%
48.934
 
< 0.1%
36.924
 
< 0.1%
Other values (20088)22008
99.8%
ValueCountFrequency (%)
6.211
< 0.1%
6.31
< 0.1%
71
< 0.1%
7.591
< 0.1%
7.871
< 0.1%
7.891
< 0.1%
7.941
< 0.1%
8.331
< 0.1%
8.771
< 0.1%
8.921
< 0.1%
ValueCountFrequency (%)
37852.051
< 0.1%
32823.531
< 0.1%
29045.351
< 0.1%
28640.751
< 0.1%
28638.861
< 0.1%
26363.51
< 0.1%
26005.251
< 0.1%
25579.41
< 0.1%
25562.11
< 0.1%
25061.751
< 0.1%

Payment_Method
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Credit Card
8813 
Debit Card
5586 
Digital Wallet
4241 
Bank Transfer
2273 
Cash on Delivery
1136 

Length

Max length16
Median length14
Mean length11.787473
Min length10

Characters and Unicode

Total characters259902
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDebit Card
2nd rowCredit Card
3rd rowCredit Card
4th rowCredit Card
5th rowDigital Wallet

Common Values

ValueCountFrequency (%)
Credit Card8813
40.0%
Debit Card5586
25.3%
Digital Wallet4241
19.2%
Bank Transfer2273
 
10.3%
Cash on Delivery1136
 
5.2%

Length

2026-02-20T21:46:33.000546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T21:46:33.057379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
card14399
31.8%
credit8813
19.5%
debit5586
 
12.3%
digital4241
 
9.4%
wallet4241
 
9.4%
bank2273
 
5.0%
transfer2273
 
5.0%
cash1136
 
2.5%
on1136
 
2.5%
delivery1136
 
2.5%

Most occurring characters

ValueCountFrequency (%)
r28894
11.1%
a28563
11.0%
C24348
9.4%
i24017
9.2%
d23212
8.9%
e23185
8.9%
23185
8.9%
t22881
8.8%
l13859
 
5.3%
D10963
 
4.2%
Other values (13)36795
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)259902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r28894
11.1%
a28563
11.0%
C24348
9.4%
i24017
9.2%
d23212
8.9%
e23185
8.9%
23185
8.9%
t22881
8.8%
l13859
 
5.3%
D10963
 
4.2%
Other values (13)36795
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)259902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r28894
11.1%
a28563
11.0%
C24348
9.4%
i24017
9.2%
d23212
8.9%
e23185
8.9%
23185
8.9%
t22881
8.8%
l13859
 
5.3%
D10963
 
4.2%
Other values (13)36795
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)259902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r28894
11.1%
a28563
11.0%
C24348
9.4%
i24017
9.2%
d23212
8.9%
e23185
8.9%
23185
8.9%
t22881
8.8%
l13859
 
5.3%
D10963
 
4.2%
Other values (13)36795
14.2%

Device_Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Mobile
12338 
Desktop
7556 
Tablet
2155 

Length

Max length7
Median length6
Mean length6.3426913
Min length6

Characters and Unicode

Total characters139850
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesktop
2nd rowDesktop
3rd rowDesktop
4th rowMobile
5th rowTablet

Common Values

ValueCountFrequency (%)
Mobile12338
56.0%
Desktop7556
34.3%
Tablet2155
 
9.8%

Length

2026-02-20T21:46:33.131698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T21:46:33.178405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mobile12338
56.0%
desktop7556
34.3%
tablet2155
 
9.8%

Most occurring characters

ValueCountFrequency (%)
e22049
15.8%
o19894
14.2%
b14493
10.4%
l14493
10.4%
M12338
8.8%
i12338
8.8%
t9711
6.9%
D7556
 
5.4%
s7556
 
5.4%
k7556
 
5.4%
Other values (3)11866
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)139850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e22049
15.8%
o19894
14.2%
b14493
10.4%
l14493
10.4%
M12338
8.8%
i12338
8.8%
t9711
6.9%
D7556
 
5.4%
s7556
 
5.4%
k7556
 
5.4%
Other values (3)11866
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)139850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e22049
15.8%
o19894
14.2%
b14493
10.4%
l14493
10.4%
M12338
8.8%
i12338
8.8%
t9711
6.9%
D7556
 
5.4%
s7556
 
5.4%
k7556
 
5.4%
Other values (3)11866
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)139850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e22049
15.8%
o19894
14.2%
b14493
10.4%
l14493
10.4%
M12338
8.8%
i12338
8.8%
t9711
6.9%
D7556
 
5.4%
s7556
 
5.4%
k7556
 
5.4%
Other values (3)11866
8.5%

Session_Duration_Minutes
Real number (ℝ)

Distinct58
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.544197
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.5 KiB
2026-02-20T21:46:33.244742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q112
median14
Q317
95-th percentile21
Maximum73
Range72
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.8625669
Coefficient of variation (CV)0.33433038
Kurtosis8.8778464
Mean14.544197
Median Absolute Deviation (MAD)2
Skewness1.4986519
Sum320685
Variance23.644556
MonotonicityNot monotonic
2026-02-20T21:46:33.327805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142576
11.7%
152551
11.6%
162227
10.1%
132198
10.0%
121868
8.5%
171851
8.4%
111359
 
6.2%
181333
 
6.0%
10982
 
4.5%
19856
 
3.9%
Other values (48)4248
19.3%
ValueCountFrequency (%)
143
 
0.2%
267
 
0.3%
3122
 
0.6%
4170
 
0.8%
5208
 
0.9%
6243
 
1.1%
7334
 
1.5%
8462
2.1%
9663
3.0%
10982
4.5%
ValueCountFrequency (%)
731
 
< 0.1%
641
 
< 0.1%
571
 
< 0.1%
552
< 0.1%
542
< 0.1%
533
< 0.1%
522
< 0.1%
512
< 0.1%
501
 
< 0.1%
493
< 0.1%

Pages_Viewed
Real number (ℝ)

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9988208
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.5 KiB
2026-02-20T21:46:33.397912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median9
Q311
95-th percentile13
Maximum24
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3942243
Coefficient of variation (CV)0.26605978
Kurtosis0.21997506
Mean8.9988208
Median Absolute Deviation (MAD)2
Skewness0.11137
Sum198415
Variance5.7323099
MonotonicityNot monotonic
2026-02-20T21:46:33.588918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
93673
16.7%
103447
15.6%
83435
15.6%
72615
11.9%
112520
11.4%
61696
7.7%
121607
7.3%
5903
 
4.1%
13882
 
4.0%
4409
 
1.9%
Other values (12)862
 
3.9%
ValueCountFrequency (%)
114
 
0.1%
233
 
0.1%
3151
 
0.7%
4409
 
1.9%
5903
 
4.1%
61696
7.7%
72615
11.9%
83435
15.6%
93673
16.7%
103447
15.6%
ValueCountFrequency (%)
241
 
< 0.1%
221
 
< 0.1%
201
 
< 0.1%
194
 
< 0.1%
188
 
< 0.1%
1725
 
0.1%
1677
 
0.3%
15172
 
0.8%
14375
1.7%
13882
4.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size193.8 KiB
True
18029 
False
4020 
ValueCountFrequency (%)
True18029
81.8%
False4020
 
18.2%
2026-02-20T21:46:33.641643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Delivery_Time_Days
Real number (ℝ)

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5021089
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.5 KiB
2026-02-20T21:46:33.688054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile13
Maximum25
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4833222
Coefficient of variation (CV)0.53572191
Kurtosis1.8473909
Mean6.5021089
Median Absolute Deviation (MAD)2
Skewness1.1276282
Sum143365
Variance12.133534
MonotonicityNot monotonic
2026-02-20T21:46:33.756971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
52899
13.1%
42886
13.1%
62673
12.1%
32549
11.6%
72197
10.0%
81750
7.9%
21449
6.6%
91423
6.5%
101024
 
4.6%
11947
 
4.3%
Other values (15)2252
10.2%
ValueCountFrequency (%)
1301
 
1.4%
21449
6.6%
32549
11.6%
42886
13.1%
52899
13.1%
62673
12.1%
72197
10.0%
81750
7.9%
91423
6.5%
101024
 
4.6%
ValueCountFrequency (%)
2510
 
< 0.1%
2412
 
0.1%
2314
 
0.1%
2216
 
0.1%
2115
 
0.1%
2044
0.2%
1937
 
0.2%
1874
0.3%
1794
0.4%
1697
0.4%

Customer_Rating
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
5
8093 
4
7608 
3
3417 
2
1916 
1
1015 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22049
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row5
4th row4
5th row4

Common Values

ValueCountFrequency (%)
58093
36.7%
47608
34.5%
33417
15.5%
21916
 
8.7%
11015
 
4.6%

Length

2026-02-20T21:46:33.826750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-20T21:46:33.891109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
58093
36.7%
47608
34.5%
33417
15.5%
21916
 
8.7%
11015
 
4.6%

Most occurring characters

ValueCountFrequency (%)
58093
36.7%
47608
34.5%
33417
15.5%
21916
 
8.7%
11015
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)22049
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
58093
36.7%
47608
34.5%
33417
15.5%
21916
 
8.7%
11015
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22049
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
58093
36.7%
47608
34.5%
33417
15.5%
21916
 
8.7%
11015
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22049
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
58093
36.7%
47608
34.5%
33417
15.5%
21916
 
8.7%
11015
 
4.6%

Interactions

2026-02-20T21:46:29.577566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.101260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.654840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.249519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.899984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.506080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.049880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.645953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.196619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.757971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.320336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.994034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.577790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.119436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.717990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.287991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.852575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.403867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.089668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.658567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.192605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.809732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.362235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.937363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.577814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.168958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.736467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.261559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.886699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.440522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.027879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.660410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.244214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.819973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.332580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.958123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.513520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.103152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.742249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.318138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.904166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.418368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:30.028336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:26.582657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.179585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:27.814574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.398141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:28.978198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-20T21:46:29.507588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-20T21:46:33.961399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeCityCustomer_RatingDelivery_Time_DaysDevice_TypeDiscount_AmountGenderIs_Returning_CustomerPages_ViewedPayment_MethodProduct_CategoryQuantitySession_Duration_MinutesTotal_AmountUnit_Price
Age1.0000.0430.000-0.0020.013-0.0020.0380.013-0.0080.0000.0120.0050.012-0.003-0.003
City0.0431.0000.0000.0000.0000.0000.0300.0230.0100.0000.0000.0060.0040.0010.000
Customer_Rating0.0000.0001.0000.0100.0060.0000.0110.0000.0000.0020.0060.0000.0000.0000.009
Delivery_Time_Days-0.0020.0000.0101.0000.0000.0020.0110.0170.0070.0100.0110.0000.004-0.007-0.006
Device_Type0.0130.0000.0060.0001.0000.0000.0070.0040.0000.0150.0000.0070.0120.0000.016
Discount_Amount-0.0020.0000.0000.0020.0001.0000.0000.014-0.0020.0130.1040.0500.0080.1110.141
Gender0.0380.0300.0110.0110.0070.0001.0000.0000.0000.0000.0070.0070.0000.0370.006
Is_Returning_Customer0.0130.0230.0000.0170.0040.0140.0001.0000.0460.0000.0000.0750.1670.0130.000
Pages_Viewed-0.0080.0100.0000.0070.000-0.0020.0000.0461.0000.0070.0000.0200.0130.0030.002
Payment_Method0.0000.0000.0020.0100.0150.0130.0000.0000.0071.0000.0000.0050.0100.0110.000
Product_Category0.0120.0000.0060.0110.0000.1040.0070.0000.0000.0001.0000.0000.0000.2090.290
Quantity0.0050.0060.0000.0000.0070.0500.0070.0750.0200.0050.0001.0000.0680.1090.000
Session_Duration_Minutes0.0120.0040.0000.0040.0120.0080.0000.1670.0130.0100.0000.0681.000-0.007-0.013
Total_Amount-0.0030.0010.000-0.0070.0000.1110.0370.0130.0030.0110.2090.109-0.0071.0000.901
Unit_Price-0.0030.0000.009-0.0060.0160.1410.0060.0000.0020.0000.2900.000-0.0130.9011.000

Missing values

2026-02-20T21:46:30.149816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-20T21:46:30.271587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Order_IDCustomer_IDDateAgeGenderCityProduct_CategoryUnit_PriceQuantityDiscount_AmountTotal_AmountPayment_MethodDevice_TypeSession_Duration_MinutesPages_ViewedIs_Returning_CustomerDelivery_Time_DaysCustomer_Rating
22048ORD_000902CUST_009022024-03-2630FemaleKonyaBooks84.30421.72315.48Debit CardDesktop89True55
22043ORD_003209CUST_032092024-03-2645MaleIzmirFood50.69111.7238.97Credit CardDesktop56False21
22038ORD_001404CUST_014042024-03-2643MaleAdanaBeauty120.6727.88233.46Credit CardDesktop108True45
22039ORD_000463CUST_004632024-03-2626MaleIstanbulFood19.8430.0059.52Credit CardMobile1110False64
22040ORD_002934CUST_029342024-03-2646MaleAnkaraToys60.12212.95107.29Digital WalletTablet227True64
22042ORD_001360CUST_013602024-03-2637FemaleBursaToys99.4610.0099.46Bank TransferDesktop59False41
22041ORD_000841CUST_008412024-03-2634OtherIstanbulBooks69.7940.00279.16Bank TransferDesktop1010False115
22044ORD_001048CUST_010482024-03-2618FemaleIzmirBeauty130.35127.30103.05Bank TransferMobile1710False91
22045ORD_001051CUST_010512024-03-2627MaleAdanaBeauty71.5510.0071.55Debit CardMobile139True64
22046ORD_003543CUST_035432024-03-2645FemaleAntalyaFood39.3815.2734.11Digital WalletMobile3810True54
Order_IDCustomer_IDDateAgeGenderCityProduct_CategoryUnit_PriceQuantityDiscount_AmountTotal_AmountPayment_MethodDevice_TypeSession_Duration_MinutesPages_ViewedIs_Returning_CustomerDelivery_Time_DaysCustomer_Rating
13963ORD_004116-1CUST_041162023-01-0130FemaleIstanbulFood33.77417.46117.62Bank TransferMobile157False34
50ORD_000018-1CUST_000182023-01-0138FemaleBursaBooks20.8450.00104.20Bank TransferMobile1710True54
1149ORD_000355-1CUST_003552023-01-0134FemaleAnkaraElectronics1254.2610.001254.26Digital WalletMobile146True105
3681ORD_001070-1CUST_010702023-01-0139MaleIstanbulBooks89.8920.00179.78Debit CardMobile1711True24
3686ORD_001073-1CUST_010732023-01-0135MaleEskisehirToys72.6950.00363.45Credit CardMobile168False81
15966ORD_004690-1CUST_046902023-01-0129FemaleIzmirElectronics834.8020.001669.60Cash on DeliveryMobile1411True65
1163ORD_000359-1CUST_003592023-01-0138FemaleAnkaraFashion107.9510.00107.95Credit CardTablet145True54
8588ORD_002510-1CUST_025102023-01-0119FemaleIstanbulHome & Garden158.8520.00317.70Digital WalletMobile147False74
3016ORD_000894-1CUST_008942023-01-0149MaleAnkaraBeauty194.32353.05529.91Debit CardMobile124False34
4843ORD_001397-1CUST_013972023-01-0118MaleAntalyaFashion51.8210.0051.82Cash on DeliveryMobile1211True43